Conceptual framework for data harmonisation in mental health using the International Classification of Functioning, Disability and Health: an example with the R2D2-MH consortium
Bibliographic record
Abstract
INTRODUCTION: Advancing research and support for neurologically diverse populations requires novel data harmonisation methods that are capable of aligning with contemporary approaches to understanding health and disability. OBJECTIVES: We present the International Classification of Functioning, Disability and Health (ICF) as a conceptual framework to support harmonisation of mental health data and present a proof of principle within the Risk and Resilience in Developmental Diversity and Mental Health (R2D2-MH) consortium. METHOD: 138 measures from various mental health datasets were linked to the ICF following the WHO's established linking rules. FINDINGS: Findings support the notion that the ICF can assist in the harmonisation of mental health data. The high level of shared ICF codes provides indications of where items may be readily harmonised to develop datasets that may align more readily with contemporary approaches to understanding health and disability. Although the linking process necessarily entails an element of subjectivity, the application of established rules can increase rigour and transparency of the harmonisation process. CONCLUSIONS: We present the first steps towards data harmonisation in mental health that is compatible with contemporary approaches in psychiatry, being more capable of capturing diversity and aligning with more transdiagnostic and neurodiversity-affirmative ways of understanding data. CLINICAL IMPLICATIONS: Our findings show promise, but future work is needed to address quantitative harmonisation. Similarly, issues related to the traditionally 'pathophysiological' frameworks that existing datasets are often embedded in can hinder the full potential of harmonisation based on the ICF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".